核心概念
Large language models face Toxic CoT problems in commonsense reasoning, leading to incorrect answers. RIDERS method effectively mitigates this issue.
要約
Large language models exhibit high-level commonsense reasoning abilities.
Toxic CoT problem causes correct answers to turn wrong.
RIDERS method compensates for information deficit in models.
RIDERS method significantly reduces Toxic CoT problems and improves reasoning performance.
Experimental validation on multiple benchmarks supports the effectiveness of RIDERS.
統計
대형 언어 모델은 공감 추론 능력을 갖추고 있습니다.
Toxic CoT 문제로 인해 올바른 답변이 잘못됩니다.
RIDERS 방법은 모델의 정보 부족을 보상합니다.
RIDERS 방법은 Toxic CoT 문제를 크게 줄이고 추론 성능을 향상시킵니다.
引用
"Large language models exhibit high-level commonsense reasoning abilities."
"RIDERS method compensates for information deficit in models."